Multiple Transform Selection Concept Modeling and Implementation Using Dynamic and Parameterized Dataflow Graphs

Abstract

The new video coding standard, Versatile Video Coding (VVC), released by the end of 2020 has increased the coding complexity both at encoder and decoder sides. This complexity increase is due to several coding tools proposed to enhance the coding efficiency. One of these tools is the Multiple Transform Selection (MTS) concept, a new approach for the transform unit. This paper aims at providing a new optimization of the MTS based on dataflow modeling. The proposed approach takes benefit of the different parallelism levels of the MTS in order to create an optimized multicore implementation. Also, this paper study the impact of the dataflow model granularity and the dynamic reconfiguration on the implementation efficiency on x86 multicore architectures. The PREESM tool is used in this study to develop the proposed dataflow models and for the granularity analysis. The dynamic reconfiguration study is here performed using the SPIDER

Automated Clustering and Pipelining of Dataflow Actors for Controlled Scheduling Complexity

“…The proposed method is applied to three image processing applications such as the OpenVVC dataflow model [4], SDP Evolutionary Pipeline (SEP), and Stereo on a SPiDF description summarize in Table I. Owing the fact that the proposed method provides a set of clustering configurations, the one that offers the best tradeoff in terms of the number of SrDAG actors, resource allocation process time, also called analysis time, latency, and throughput speedups, and memory footprint growth is chosen, noted BC for Best Clustering results.…”